Google DeepMind has introduced GenCast, an advanced AI model designed to revolutionise weather forecasting by delivering faster, more accurate predictions of weather uncertainties and risks up to 15 days ahead.

What is GenCast and How Does it Work?

GenCast is a diffusion-based generative AI model (one that transforms ‘noisy’ data into realistic outputs), a sophisticated approach typically used in creating high-quality images, videos, and music. In the realm of weather forecasting, it works by leveraging decades of historical meteorological data to simulate complex atmospheric dynamics. Trained on nearly 40 years of data from the European Centre for Medium-Range Weather Forecasts (ECMWF), GenCast can generate an ensemble of predictions, providing a probabilistic range of possible weather outcomes rather than a single deterministic forecast.

Google’s DeepMind explains why this ‘ensemble forecasting’ is essential, saying: “Because a perfect weather forecast is not possible, scientists and weather agencies use probabilistic ensemble forecasts, where the model predicts a range of likely weather scenarios. Such ensemble forecasts are more useful than relying on a single forecast, as they provide decision-makers with a fuller picture of possible weather conditions in the coming days and weeks and how likely each scenario is.”

Identifies Past Patterns to Predict Future Weather

Unlike traditional numerical weather prediction models, which rely heavily on computationally intensive physics-based equations, GenCast’s data-driven methodology identifies patterns in past weather events to forecast future scenarios. This unique approach allows it to outperform conventional systems, particularly in predicting extreme weather conditions like cyclones and storms, which are notoriously challenging for standard models.

Key Features and Capabilities

One of the standout features of GenCast is its ability to produce high-quality forecasts with remarkable speed. For example, as Google DeepMind points out: “It takes a single Google Cloud TPU v5 just 8 minutes to produce one 15-day forecast” (a Google Cloud TPU v5 is a specialised chip for accelerating AI computations). In contrast, traditional models often need many hours of processing time on supercomputers equipped with thousands of processors.

‘Ensemble’ Forecasting Method Means Better Forecasting

GenCast’s ensemble forecasting method, i.e. producing multiple plausible weather scenarios, may enable meteorologists and decision-makers to assess risks and uncertainties more comprehensively. This capability could prove to be crucial for industries and communities that rely on understanding the full spectrum of potential weather outcomes, particularly in today’s context of climate change and increasingly volatile weather patterns. As DeepMind says: “Better forecasts of extreme weather, such as heat waves or strong winds, enable timely and cost-effective preventative actions. GenCast offers greater value than ENS when making decisions about preparations for extreme weather, across a wide range of decision-making scenarios.”

Who Could Benefit from GenCast?

The versatility of GenCast makes it a real game-changer for a wide array of sectors, such as:

– Disaster management. Early and accurate predictions of extreme weather events allow governments and humanitarian organisations to plan evacuations, allocate resources, and mitigate damages more effectively.

– The energy sector. Renewable energy providers, particularly those in wind and solar power, can optimise energy generation and grid management based on precise weather forecasts.

– Agriculture and fisheries. Farmers and fishermen can better plan planting, harvesting, and fishing schedules, reducing losses due to unforeseen weather disruptions.

– Transportation and logistics. Airlines, shipping companies, and logistics providers can enhance operational efficiency and safety by anticipating weather conditions that may impact travel and delivery routes.

Impact on Futures Markets

GenCast may also hold promise for futures markets, e.g. in agriculture and commodities trading. For example, weather fluctuations heavily influence the supply and pricing of essential goods such as grains, pork bellies, and seafood. By providing early and accurate predictions, GenCast may enable traders to make more informed decisions, thereby stabilising markets and reducing volatility. Knowledge of an impending drought could, for example, prompt strategic planning, such as stockpiling or diversifying supply chains, to minimise financial losses.

A New Benchmark AI and Weather Forecasting?

While AI-driven weather prediction is not entirely new, GenCast’s performance appears to set a new benchmark. As DeepMind says: “GenCast showed better forecasting skill than ECMWF’s ENS, the top operational ensemble forecasting system that many national and local decisions depend upon every day. GenCast was more accurate than ENS on 97.2 per cent of these targets, and on 99.8 per cent at lead times greater than 36 hours.”

IBM’s Watson has previously ventured into this space with its weather-focused AI, but GenCast’s ability to forecast medium-range weather events and extreme conditions with superior accuracy looks like positioning it as today’s frontrunner.

Challenges and Criticisms

Despite its groundbreaking capabilities, GenCast is not without its challenges. For example:

Issues with resolution and local accuracy. GenCast operates at a lower resolution compared to some traditional numerical models, potentially limiting its precision for localised weather forecasts.

Integration with existing systems. Adoption of GenCast requires validation and acceptance by meteorological agencies, which must assess its reliability before integrating it into their systems.

Possible data limitations. GenCast depends on historical data, and its effectiveness may be constrained in regions with sparse datasets or when predicting unprecedented weather patterns driven by climate change.

Collaboration Between AI and Traditional Still Important

Although GenCast appears to bring a unique new and powerful method to weather forecasting, DeepMind is keen to point out that it’s not going to be a case of AI replacing all traditional methods, rather GenCast will be used as part of a collaboration between AI and traditional meteorology. DeepMind says: “We deeply value our partnerships with weather agencies, and will continue working with them to develop AI-based methods that enhance their forecasting. Meanwhile, traditional models remain essential for this work. This cooperation between AI and traditional meteorology highlights the power of a combined approach to improve forecasts and better serve society.”

What Could It Mean for the AI Sector?

GenCast’s promise highlights the transformative potential of AI in addressing complex global challenges. Its application in weather forecasting is another demonstration of the adaptability of generative AI, traditionally associated with creative industries, to scientific and practical domains. To advance its mission and hopes for the wide adoption of and collaborations with GenCast in the weather and climate community, DeepMind says it has “Made GenCast an open model and released its code and weights.”

At the same time, the introduction of GenCast raises the stakes for competitors. Companies aiming to replicate or surpass DeepMind’s achievements will need to tackle significant technical and computational hurdles. This competition could lead to even greater advancements in AI technology and its applications.

What Does This Mean for Your Business?

GenCast looks like being a remarkable leap forward in the integration of AI into weather forecasting. Its ability to provide accurate, probabilistic forecasts with unprecedented speed and efficiency appears to have set a new standard for the industry. By leveraging decades of historical data, and using the ‘ensemble’ forecasting method, GenCast can deliver insights that are not only scientifically impressive but may also be critically important in addressing real-world challenges. From disaster management and renewable energy planning to agriculture and futures trading, the potential benefits span a wide range of sectors.

However, as with any innovation, GenCast is not without its limitations. Its relatively lower resolution compared to some traditional models may restrict its utility in highly localised scenarios, and its reliance on historical data could pose challenges in areas with sparse records or in predicting unprecedented climate-driven phenomena. These constraints highlight the ongoing importance of collaboration between AI and traditional meteorological approaches, as DeepMind acknowledges. Their insistence on combining AI-based methods with existing systems demonstrates a pragmatic understanding that AI, while transformative, may not be a silver bullet.

The model’s openness, with its code and weights made publicly available, also signals a commitment to advancing the wider weather and climate community. This transparency may be a helpful way to both foster collaboration and ensure that GenCast can be scrutinised, validated, and improved upon by the global scientific community.

GenCast’s emergence is also likely to intensify competition within the AI sector. As other companies and research institutions strive to match or surpass its capabilities, the pace of innovation in AI-based weather prediction and other real-world applications could accelerate. This competition may benefit society at large by driving further advancements in technology and expanding the possibilities for AI integration across industries.

GenCast, therefore, is a vivid example of how AI can be harnessed to address some of the most pressing global challenges. While there is still room for refinement and integration, its launch signifies a future where advanced technologies like AI play a crucial role in safeguarding lives, improving efficiency, and fostering a deeper understanding of our planet’s dynamic systems.

Apple Inc. is reportedly facing a lawsuit from employee Amar Bhakta, who alleges the company unlawfully monitors employees’ personal devices and iCloud accounts.

Filed in California state court, the lawsuit claims Apple mandates employees to install software on personal devices used for work, granting the company access to personal data such as emails, photos, and health information. Also, it alleges that Apple’s policies permit surveillance of employees even when off duty and within their homes.

Bhakta, part of Apple’s digital advertising team since 2020, asserts that these policies have adversely affected his career opportunities. He claims he was prohibited from public speaking engagements related to digital advertising and was instructed to remove job-related information from his LinkedIn profile.

Apple has refuted the allegations, stating that the claims lack merit. The company has emphasised its commitment to employee rights, noting that all employees receive annual training on their rights to discuss wages, hours, and working conditions.

This case highlights the ongoing debate over employer access to employees’ personal devices and data, especially as the lines between work and personal life become increasingly blurred. For businesses, it highlights the importance of establishing clear, lawful policies regarding employee privacy and device usage to maintain trust and comply with legal standards.

Also, the outcome of this lawsuit could set a significant precedent, potentially influencing corporate practices concerning employee monitoring and privacy rights, and prompting companies to reassess their policies to ensure they respect employee privacy while safeguarding corporate interests.

Romania’s Constitutional Court has annulled the first round of its presidential election due to allegations of Russian interference via TikTok.

Far-right candidate Călin Georgescu, a pro-Russian figure, had won with 23 per cent of the vote. Intelligence revealed a sophisticated influence operation involving over 25,000 TikTok accounts, which amplified Georgescu’s campaign, garnering 52 million video views in just four days. TikTok denies any preferential treatment but is under EU scrutiny to provide data on its algorithms and counter-disinformation measures.

Outgoing Prime Minister Marcel Ciolacu supported the annulment as vital for national security, while Georgescu called it a “formalised coup d’état.” The annulment leaves Romania in political limbo, with no set timeline for a new election.

This case highlights the risk of foreign interference via social media. Businesses should bolster cybersecurity, monitor for disinformation, and collaborate with platforms to counter coordinated inauthentic behaviour and protect digital integrity.

Amazon Web Services (AWS) is to pilot a new AI-designed carbon-removal material at one of its data centres as part of a new strategic partnership with AI start-up Orbital Materials.

Why?

As data processing and storage requirements increase, data centres must handle increasingly complex AI workloads, pushing their energy and cooling demands ever higher. AWS, like other operators, has set ambitious carbon reduction targets, but purchasing offsets can be costly and less transparent. In a new move, partnering with Orbital and integrating a new carbon-removal material at an AWS data centre by 2025, the company is aiming to directly remove more CO₂ from its airflow than it produces, potentially at a lower cost than traditional offsets. It’s hoped that this approach will not only help AWS meet its sustainability commitments but also address the escalating operational and environmental pressures driving these changes.

Who is Orbital and What is the AWS Deal?

Orbital, launched at the end of 2022 and led by CEO Jonathan Godwin, operates from facilities in Princeton, New Jersey and London. The start-up uses an AI-driven platform to rapidly discover and test advanced materials for climate-focused solutions (work that would traditionally take years in a lab). According to Amazon’s website, since establishing its research and development lab in early 2024, Orbital has seen a tenfold improvement in its carbon-removal material’s performance, highlighting the revolutionary potential of AI-driven materials discovery.

Through its multi-year partnership with AWS, Orbital will supply a carbon-removal material for integration at an AWS data centre by 2025. The goal is to capture more CO₂ than the facility emits, helping AWS meet its carbon reduction targets and potentially offering a more cost-effective, transparent alternative to traditional offsets.

Carbon Removal at the Source

The principal idea behind the AWS–Orbital collaboration is to use data centres themselves as a platform for direct carbon capture. Data centres rely on vast, sophisticated cooling systems to maintain the optimal temperatures required by the thousands of servers inside. These cooling systems constantly circulate large volumes of air, providing an excellent opportunity to integrate a carbon-removal material that can filter out CO₂ molecules as they flow through.

How Does Orbital’s Carbon Removal Material Work?

Orbital’s CEO, Jonathan Godwin, recently explained the nature of the advanced carbon-removing material it produces, describing it as “like a sponge at the atomic level”. For example, the material’s tiny cavities are sized to interact specifically with CO₂, thereby allowing it to trap the gas while letting other, less harmful components of the air pass freely. By 2025, AWS plans to pilot this cutting-edge carbon-removal technology in one of its data centres, testing its scalability and real-world performance.

A More Cost-Effective Alternative

While some operators resort to carbon offsets to reduce their net emissions, these can be expensive and often involve complex verification processes. By capturing carbon directly from the air at the source, data centres could theoretically bypass intermediaries and reduce their reliance on offset markets. According to Jonathan Godwin, the added cost of incorporating Orbital’s carbon-removal material amounts to roughly 10 per cent of the hourly charge of renting a GPU chip for AI training, significantly less than the price of most carbon offsets. This cost-effectiveness could make the proposition commercially attractive, helping data centre operators improve their environmental performance without eroding their bottom line.

Efficiency and Water Usage

While reducing CO₂ emissions is a crucial goal, the AWS–Orbital partnership also aims to tackle other environmental challenges associated with large-scale computing infrastructure. For example, data centres are thirsty operations, requiring huge amounts of water to maintain their cooling systems. Therefore, the ability to integrate more efficient, high-performance materials into cooling processes could lead to reductions in both energy and water consumption.

Speaking about the partnership (on the Amazon website), Orbital’s CEO Jonathan Godwin said, “Our partnership with AWS will accelerate the deployment of our advanced technologies for data centre decarbonisation and efficiency. Working with the market-leading AWS team will accelerate our development of products in cooling, water utilisation, and carbon removal.” In a similar vein, Howard Gefen, General Manager of AWS Energy & Utilities, stated, “AWS looks forward to collaborating with Orbital and their mission to drive data centre decarbonisation and efficiency.”

By designing materials that can capture carbon, improve cooling efficiency, and potentially reduce water consumption, Orbital’s platform looks as though it could open new pathways for sustainable data centre operations. The success of these early trials could lead the way to more widespread adoption of such materials throughout the data centre industry.

Technical and Logistical Challenges

Of course, the introduction of any new technology brings its own challenges. For example, trying to integrate an advanced filtration material into a complex data centre cooling system will alter airflow characteristics. Although this change could increase the workload on existing fans and pumps, Orbital believes the net effect will be positive. Also, the slightly higher energy required for pumping air through the new filters should be more than compensated for by the benefits of lower emissions and improved resource efficiency.

Another pressing consideration is handling the captured CO₂. Once the gas is isolated from the airstream, what then? While specific details of exactly how the carbon will be stored or reused are currently not being made clear, the partners will, no doubt, need robust protocols for managing the extracted greenhouse gases sustainably. Ensuring safe, long-term storage or practical utilisation of this captured carbon is likely to be key to the project’s overall success.

Not The Only One Involved In Data Centre Carbon Capture

It should be noted here that Orbital is not alone in pursuing on-site carbon capture in data centres. For example, other tech giants such as Alphabet (Google) and Meta have filed patents related to similar concepts, and start-ups like 280 Earth are also working on solutions to tackle data centre emissions at source. However, what appears to distinguish Orbital’s approach is its ability to move fast and iterate quickly. By using generative AI to design and test materials virtually, Orbital can arrive at promising formulations far faster than traditional lab-based methods.

This accelerated materials discovery process looks like giving Orbital a potential edge in developing specialised compounds. For example, its carbon-removal material is tailored to work effectively with hot, CO₂-laden air exiting data centre servers. Rather than building a generic carbon filter, Orbital can produce optimised materials that function well under real-world operational conditions.

Wider Applications and Open Access to AI Models

Beyond this single pilot project, Orbital’s technology could also have a much broader impact. For example, the start-up plans to make its open-source AI model ‘Orb’ available to AWS customers via Amazon SageMaker JumpStart and AWS Marketplace. This means that other companies tackling their own materials and climate challenges, whether in semiconductors, batteries, or electronics, will soon have a powerful new tool at their disposal.

Such accessibility is critical. Orbital’s AI-driven approach, therefore, does not appear to just offer one clever solution to a pressing sustainability issue, but could represent a new methodology for discovering and optimising advanced materials. By making these capabilities available in the cloud, Orbital and AWS hope to democratise materials R&D, thereby, hopefully, empowering a wider range of enterprises to contribute to sustainability-driven innovation.

Keeping Pace with Sustainability Targets

The urgency driving projects also comes from the large technology companies having pledged to reach net-zero carbon emissions within the coming decades. Yet, as AI models grow more complex, requiring ever more computational power, energy usage soars. Without new interventions, these data centres risk undermining carefully set climate targets.

AWS, as the world’s largest cloud-computing provider by revenue, is under particular scrutiny. Millions of customers rely on its infrastructure, and sustainability commitments have become a point of competitive differentiation. By embracing on-site carbon capture and making advanced materials more accessible, AWS is banking on not only working to meet its own targets but potentially setting a precedent that others in the industry may follow.

Potential Ripple Effects Across the Sector

If the AWS pilot proves successful, it could catalyse a wave of adoption in data centres across the globe. On-site carbon capture may offer a more transparent and reliable way of verifying emissions reductions than conventional offsets. It might even allow data centre operators to generate their own carbon credits by capturing more CO₂ than they produce, thereby transforming a cost centre into a revenue stream.

Such a shift would, however, require careful economic, regulatory, and environmental considerations. For now, the AWS–Orbital initiative is a test (albeit part of a “multi-year” commitment), but one that carries high stakes and considerable promise. This early pilot could be said to represent a proactive step towards embedding sustainability at the heart of AI-driven infrastructure and an opportunity to ensure that the digital revolution does not come at an unacceptable environmental cost.

What Does This Mean For Your Organisation?

In many ways, the AWS–Orbital pilot project encapsulates the evolving relationship between digital infrastructure and the urgent need to address our environmental responsibilities. By attempting to capture carbon on-site rather than relying solely on offsets, AWS is exploring a pathway that could be more transparent, cost-effective, and efficient. Orbital’s rapid, AI-driven approach to materials discovery highlights a significant shift in how quickly breakthroughs can be achieved, and the involvement of AWS, arguably one of the most influential players in the sector, puts added weight behind this experimentation.

However, the path forward is not going to be without its hurdles. For example, integrating new materials into data centres, ensuring that carbon can be meaningfully stored or reused, and consistently meeting demanding performance standards will all require careful planning and meticulous execution. Also, the costs, although promising at present, are likely to evolve alongside technological improvements and market conditions, meaning that careful economic analysis will remain crucial.

Beyond this specific partnership (due to last an unspecified, but probably a small number of years), it suggests that the integration of advanced materials and AI-driven R&D could help carve out a more sustainable future for data centres worldwide.

As talking to AI is set to become mainstream, this video takes a look at speaking directly with ChatGPT and includes a couple of examples of what it can be used for. It’s expected this will mode of interaction will become second nature very soon.

[Note – To Watch This Video without glitches/interruptions, It may be best to download it first]

With many users adopting dark mode in the new Microsoft Outlook, including a transparent background for images such as logos or signatures ensures your emails look professional across both light and dark themes.

Why It Matters:

– Many users enable dark mode, which displays emails on a black or dark-coloured background.

– Images with a white background appear as distracting blocks against the dark background, disrupting the email’s appearance.

– Transparent images seamlessly adapt to both light and dark modes, maintaining a polished look.

How to Use Transparent Background Images:
Create a Transparent Image:

– Use a design tool (e.g., Photoshop, Canva, or any free online editor) to remove the background from your image.

– Save the file as a PNG format to preserve transparency.

Add the Transparent Image to Your Email Signature:

– Open Outlook and go to Settings > View all Outlook settings > Mail > Compose and reply.

– Under the Email signature section, upload the transparent PNG image and adjust its placement.

Test Your Email in Dark Mode:

– Send yourself a test email and view it in both light and dark modes to ensure the image displays correctly.